Abstract: The most influential programming paradigm today
is object oriented (OO) programming and it is widely used in
education and industry. Recognizing the importance of equipping
students with OO knowledge and skills, it is not surprising that most
Computer Science degree programs offer OO-related courses. How
do we assess whether the students have acquired the right objectoriented
skills after they have completed their OO courses? What are
object oriented skills? Currently none of the current assessment
techniques would be able to provide this answer. Traditional forms of
OO programming assessment provide a ways for assigning numerical
scores to determine letter grades. But this rarely reveals information
about how students actually understand OO concept. It appears
reasonable that a better understanding of how to define and assess
OO skills is needed by developing a criterion referenced model. It is
even critical in the context of Malaysia where there is currently a
growing concern over the level of competency of Malaysian IT
graduates in object oriented programming. This paper discussed the
approach used to develop the criterion-referenced assessment model.
The model can serve as a guideline when conducting OO
programming assessment as mentioned. The proposed model is
derived by using Goal Questions Metrics methodology, which helps
formulate the metrics of interest. It concluded with a few suggestions
for further study.
Abstract: This paper presents a new method to detect high impedance faults in radial distribution systems. Magnitudes of third and fifth harmonic components of voltages and currents are used as a feature vector for fault discrimination. The proposed methodology uses a learning vector quantization (LVQ) neural network as a classifier for identifying high impedance arc-type faults. The network learns from the data obtained from simulation of a simple radial system under different fault and system conditions. Compared to a feed-forward neural network, a properly tuned LVQ network gives quicker response.
Abstract: With the explosive growth of data available on the
Internet, personalization of this information space become a
necessity. At present time with the rapid increasing popularity of the
WWW, Websites are playing a crucial role to convey knowledge and
information to the end users. Discovering hidden and meaningful
information about Web users usage patterns is critical to determine
effective marketing strategies to optimize the Web server usage for
accommodating future growth. The task of mining useful information
becomes more challenging when the Web traffic volume is enormous
and keeps on growing. In this paper, we propose a intelligent model
to discover and analyze useful knowledge from the available Web
log data.
Abstract: Global temperature had increased by about 0.5oC over
the past century, increasing temperature leads to a loss or a decrease
of soil organic matter (SOM). Whereas soil organic matter in many
tropical soils is less stable than that of temperate soils, and it will be
easily affected by climate change. Therefore, conservation of soil
organic matter is urgent issue nowadays. This paper presents the
effect of different doses (5%, 15%) of Ca-type zeolite in conjunction
with organic manure, applied to soil samples from Philippines,
Paraguay and Japan, on the decomposition resistance of soil organic
matter under high temperature. Results showed that a remain or
slightly increase the C/N ratio of soil. There are an increase in
percent of humic acid (PQ) that extracted with Na4P2O7. A decrease
of percent of free humus (fH) after incubation was determined. A
larger the relative color intensity (RF) value and a lower the color
coefficient (6logK) value following increasing zeolite rates leading
to a higher degrees of humification. The increase in the aromatic
condensation of humic acid (HA) after incubation, as indicates by the
decrease of H/C and O/C ratios of HA. This finding indicates that the
use of zeolite could be beneficial with respect to SOM conservation
under global warming condition.
Abstract: Intelligent systems are required in order to quickly and accurately analyze enormous quantities of data in the Internet environment. In intelligent systems, information extracting processes can be divided into supervised learning and unsupervised learning. This paper investigates intelligent clustering by unsupervised learning. Intelligent clustering is the clustering system which determines the clustering model for data analysis and evaluates results by itself. This system can make a clustering model more rapidly, objectively and accurately than an analyzer. The methodology for the automatic clustering intelligent system is a multi-agent system that comprises a clustering agent and a cluster performance evaluation agent. An agent exchanges information about clusters with another agent and the system determines the optimal cluster number through this information. Experiments using data sets in the UCI Machine Repository are performed in order to prove the validity of the system.
Abstract: In Data mining, Fuzzy clustering algorithms have
demonstrated advantage over crisp clustering algorithms in dealing
with the challenges posed by large collections of vague and uncertain
natural data. This paper reviews concept of fuzzy logic and fuzzy
clustering. The classical fuzzy c-means algorithm is presented and its
limitations are highlighted. Based on the study of the fuzzy c-means
algorithm and its extensions, we propose a modification to the cmeans
algorithm to overcome the limitations of it in calculating the
new cluster centers and in finding the membership values with
natural data. The efficiency of the new modified method is
demonstrated on real data collected for Bhutan-s Gross National
Happiness (GNH) program.
Abstract: Axial Flux Permanent Magnet (AFPM) Machines require effective cooling due to their high power density. The detrimental effects of overheating such as degradation of the insulation materials, magnets demagnetization, and increase of Joule losses are well known. This paper describes the CFD simulations performed on a test rig model of an air cooled Axial Flux Permanent Magnet (AFPM) generator built at Durham University to identify the temperatures and heat transfer coefficient on the stator. The Reynolds Averaged Navier-Stokes and the Energy equations are solved and the flow pattern and heat transfer developing inside the machine are described. The Nusselt number on the stator surfaces has been found. The dependency of the heat transfer on the flow field is described temperature field obtained. Tests on an experimental are undergoing in order to validate the CFD results.
Abstract: This paper presents the results related to the
interference reduction technique in multistage multiuser detector for
asynchronous DS-CDMA system. To meet the real-time
requirements for asynchronous multiuser detection, a bit streaming,
cascade architecture is used. An asynchronous multiuser detection
involves block-based computations and matrix inversions. The paper
covers iterative-based suboptimal schemes that have been studied to
decrease the computational complexity, eliminate the need for matrix
inversions, decreases the execution time, reduces the memory
requirements and uses joint estimation and detection process that
gives better performance than the independent parameter estimation
method. The stages of the iteration use cascaded and bits processed
in a streaming fashion. The simulation has been carried out for
asynchronous DS-CDMA system by varying one parameter, i.e.,
number of users. The simulation result exhibits that system gives
optimum bit error rate (BER) at 3rd stage for 15-users.
Abstract: A modified Genetic Algorithm (GA) based optimal selection of parameters for Automatic Generation Control (AGC) of multi-area electric energy systems is proposed in this paper. Simulations on multi-area reheat thermal system with and without consideration of nonlinearity like governor dead band followed by 1% step load perturbation is performed to exemplify the optimum parameter search. In this proposed method, a modified Genetic Algorithm is proposed where one point crossover with modification is employed. Positional dependency in respect of crossing site helps to maintain diversity of search point as well as exploitation of already known optimum value. This makes a trade-off between exploration and exploitation of search space to find global optimum in less number of generations. The proposed GA along with decomposition technique as developed has been used to obtain the optimum megawatt frequency control of multi-area electric energy systems. Time-domain simulations are conducted with trapezoidal integration along with decomposition technique. The superiority of the proposed method over existing one is verified from simulations and comparisons.
Abstract: Due to the low heavy metal concentrations, the
bottom ash from a 32 MW municipal district heating plant was
determined to be a potential forest fertilizer as such. However,
additional Ca would be needed, because its Ca concentration of 1.9-
% (d.w.) was lower than the statutory Finnish minimum limit value
of 6.0-% (d.w.) for Ca in forest fertilizer. Due to the elevated As
concentration (53.0 mg/kg; d.w.) in the fly ash from the 32 MW
municipal district heating plant, and Cr concentration (620 mg/kg;
d.w.) in the ash fraction (i.e. mixture of the bottom ash and fly ash)
from the 6 MW municipal district heating plant, which exceed the
limit values of 30 mg/kg (d.w.) and 300 mg/kg (d.w.) for As and Cr,
respectively, these residues are not suitable as forest fertilizers.
Although these ash fractions cannot be used as a forest fertilizer as
such, they can be used for the landscaping of landfills or in industrial
and other areas that are closed to the public. However, an
environmental permit is then needed.
Abstract: In today-s modern world, the number of vehicles is
increasing on the road. This causes more people to choose walking
instead of traveling using vehicles. Thus, proper planning of
pedestrians- paths is important to ensure the safety of pedestrians in a
walking area. Crowd dynamics study the pedestrians- behavior and
modeling pedestrians- movement to ensure safety in their walking paths.
To date, many models have been designed to ease pedestrians-
movement. The Social Force Model is widely used among researchers
as it is simpler and provides better simulation results. We will discuss
the problem regarding the ritual of circumambulating the Ka-aba
(Tawaf) where the entrances to this area are usually congested which
worsens during the Hajj season. We will use the computer simulation
model SimWalk which is based on the Social Force Model to simulate
the movement of pilgrims in the Tawaf area. We will first discuss the
effect of uni and bi-directional flows at the gates. We will then restrict
certain gates to the area as the entrances only and others as exits only.
From the simulations, we will study the effect of the distance of other
entrances from the beginning line and their effects on the duration of
pilgrims circumambulate Ka-aba. We will distribute the pilgrims at the
different entrances evenly so that the congestion at the entrances can be
reduced. We would also discuss the various locations and designs of
barriers at the exits and its effect on the time taken for the pilgrims to
exit the Tawaf area.
Abstract: Segmentation of Magnetic Resonance Imaging (MRI) images is the most challenging problems in medical imaging. This paper compares the performances of Seed-Based Region Growing (SBRG), Adaptive Network-Based Fuzzy Inference System (ANFIS) and Fuzzy c-Means (FCM) in brain abnormalities segmentation. Controlled experimental data is used, which designed in such a way that prior knowledge of the size of the abnormalities are known. This is done by cutting various sizes of abnormalities and pasting it onto normal brain tissues. The normal tissues or the background are divided into three different categories. The segmentation is done with fifty seven data of each category. The knowledge of the size of the abnormalities by the number of pixels are then compared with segmentation results of three techniques proposed. It was proven that the ANFIS returns the best segmentation performances in light abnormalities, whereas the SBRG on the other hand performed well in dark abnormalities segmentation.
Abstract: In this paper, by introducing twice continuously differentiable mappings, we develop an interior path following following method, which enables us to give a constructive proof of the general Brouwer fixed point theorem and thus to solve fixed point problems in a class of non-convex sets. Under suitable conditions, a smooth path can be proven to exist. This can lead to an implementable globally convergent algorithm. Several numerical examples are given to illustrate the results of this paper.
Abstract: The main purpose of this research aimed to create tactile texture designed media for the blind used for extra learning outside classrooms in order to enhance imagination of the blind about Himmapan creatures, furthermore, the main objective of the research focused on improving the visual disabled perception to be equal to normal people. The target group of the research is blinded students studying in The Bangkok school for the blind between grade 4-6 in the second semester of 2011 who are able to read the braille language. The research methodology consisted of the field study and the documentary study related to the blind, tactile texture designed media and Himmapan creatures. 10 pictures of tactile texture designed media were created in the designing process which began after the analysis had conducted based the primary and secondary data. The works had presented to experts in the visual disabled field who evaluated the works. After approval, the works used as prototype to teach the blind. KeywordsBlind, Himmapan Creatures, Tactile Texture.
Abstract: This paper draws a methodological framework adopted within an internal Telecomitalia project aimed to identify, on a user centred base, the potential interest towards a technological scenario aimed to extend on a personal bubble the typical communication and media fruition home environment. The problem is that involving user in the early stage of the development of such disruptive technology scenario asking users opinions on something that users actually do not manage even in a rough manner could lead to wrong or distorted results. For that reason we chose an approach that indirectly aim to understand users hidden needs in order to obtain a meaningful picture of the possible interest for a technological proposition non yet easily understandable.
Abstract: The Stokes equation connected with the fluid flow
over the axisymmetric bodies in a cylindrical area is considered. The
equation is studied in a moving coordinate system with the
appropriate boundary conditions. Effective formulas for the velocity
components are obtained. The graphs of the velocity components and
velocity profile are plotted.
Abstract: We present new finite element methods for Helmholtz and Maxwell equations on general three-dimensional polyhedral meshes, based on domain decomposition with boundary elements on the surfaces of the polyhedral volume elements. The methods use the lowest-order polynomial spaces and produce sparse, symmetric linear systems despite the use of boundary elements. Moreover, piecewise constant coefficients are admissible. The resulting approximation on the element surfaces can be extended throughout the domain via representation formulas. Numerical experiments confirm that the convergence behavior on tetrahedral meshes is comparable to that of standard finite element methods, and equally good performance is attained on more general meshes.
Abstract: This paper investigates how the use of machine learning techniques can significantly predict the three major dimensions of learner-s emotions (pleasure, arousal and dominance) from brainwaves. This study has adopted an experimentation in which participants were exposed to a set of pictures from the International Affective Picture System (IAPS) while their electrical brain activity was recorded with an electroencephalogram (EEG). The pictures were already rated in a previous study via the affective rating system Self-Assessment Manikin (SAM) to assess the three dimensions of pleasure, arousal, and dominance. For each picture, we took the mean of these values for all subjects used in this previous study and associated them to the recorded brainwaves of the participants in our study. Correlation and regression analyses confirmed the hypothesis that brainwave measures could significantly predict emotional dimensions. This can be very useful in the case of impassive, taciturn or disabled learners. Standard classification techniques were used to assess the reliability of the automatic detection of learners- three major dimensions from the brainwaves. We discuss the results and the pertinence of such a method to assess learner-s emotions and integrate it into a brainwavesensing Intelligent Tutoring System.
Abstract: This study empirically examines the long run equilibrium relationship between South Africa’s exports and imports using quarterly data from 1985 to 2012. The theoretical framework used for the study is based on Johansen’s Maximum Likelihood cointegration technique which tests for both the existence and number of cointegration vectors that exists. The study finds that both the series are integrated of order one and are cointegrated. A statistically significant cointegrating relationship is found to exist between exports and imports. The study models this unique linear and lagged relationship using a Vector Error Correction Model (VECM). The findings of the study confirm the existence of a long run equilibrium relationship between exports and imports.
Abstract: This article presents the development of a neural
network cognitive model for the classification and detection of
different frequency signals. The basic structure of the implemented
neural network was inspired on the perception process that humans
generally make in order to visually distinguish between high and low
frequency signals. It is based on the dynamic neural network concept,
with delays. A special two-layer feedforward neural net structure was
successfully implemented, trained and validated, to achieve
minimum target error. Training confirmed that this neural net
structure descents and converges to a human perception classification
solution, even when far away from the target.